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SHF: Small: Collaborative Research: Accelerated Data Transformation: A Software-Hardware Stack for Transducers

SHF：小型：协作研究：加速数据转换：传感器的软件硬件堆栈

基本信息

批准号：
1909364
负责人：
Andrew Chien
金额：
$ 26.6万
依托单位：
University of Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909364&HistoricalAwards=false
关键词：
SHF Small Collaborative Research Accelerated

项目摘要

Recent years have seen an explosive rise of "big data" and data-intensive computing. Many scientific and data analytics applications that operate on large data sets perform data transformation at their core. For example, many genomics applications translate DNA sequences into protein sequences and must perform this transformation on large volumes of data (petabytes) generated by DNA sequencers. Recent studies have shown that popular data analytics systems spend significant amount of time performing data transformation operations such as data compression, decompression, serialization, deserialization and error correction. While application-specific hardware accelerators can be useful, their narrow applicability can significantly limit their impact. On the other hand, accelerating a common computation at the core of many applications can have a broader impact, and benefit not only existing, but also future applications. This research targets the problem of general acceleration of data transformation. More specifically, to allow breadth of utility, the project aims to provide a software-hardware stack to accelerate the computational abstraction at the core of data transformation, namely, finite-state transducers. Given the societal importance of big data computing, a significant broader impact of this work is the uptake of research ideas and technology into the scientific base, and their resulting impact on a wide range of 'big data' applications for science, industry, and society. In addition, this project allows students to experience in first hand how abstract concepts such as finite-state transducers can be applied to practical problems, connecting elements of theory of computation, algorithm design and optimization, applications and systems architecture.The research investigates the transducers computational model and its efficient implementation with the goal of providing performance and energy-efficiency gains in data analytics systems all of which rely on data transformation. In particular, this work aims to reduce transducer theory to practical use by mapping transducer programs onto emerging data processing accelerators. To this end, this work targets the following issues. First, design a software stack to map transducers onto novel hardware accelerators. In particular, the investigators build on their previous work on the design and implementation of the Unstructured Data Processor, a novel hardware accelerator for data transformation shown to give high performance, but that at present lacks a high-level programming model. Accomplishing this goal requires investigating a set of platform-independent and platform-specific optimizations aimed to minimize the code size, minimize the memory utilization, and leverage the coarse- and fine-grained parallelism inherent in the computation. Second, improve and extend the underlying hardware accelerator based on the insights acquired in the design of the software stack. Third, extend the transducer model to express the full range of data transformations in popular data analytics systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，“大数据”和数据密集型计算出现了爆炸性增长。许多在大型数据集上运行的科学和数据分析应用程序在其核心执行数据转换。例如，许多基因组学应用程序将DNA序列转换为蛋白质序列，并且必须对DNA测序仪生成的大量数据(PB)执行这种转换。最近的研究表明，流行的数据分析系统花费大量的时间执行数据转换操作，如数据压缩、解压缩、序列化、反序列化和纠错。虽然特定于应用程序的硬件加速器可能很有用，但其有限的适用性可能会显著限制其影响。另一方面，加速许多应用程序核心的公共计算可以产生更广泛的影响，不仅对现有的应用程序有利，而且对未来的应用程序也有好处。本研究针对的是数据转换的普遍加速问题。更具体地说，为了扩大实用范围，该项目旨在提供一个软硬件堆栈，以加速数据转换核心即有限状态换能器的计算抽象。鉴于大数据计算的社会重要性，这项工作的一个重要的更广泛的影响是将研究想法和技术吸收到科学基础中，并由此对科学、工业和社会的广泛的“大数据”应用产生影响。此外，该项目允许学生亲身体验如何将有限状态换能器等抽象概念应用于实际问题，将计算理论、算法设计和优化、应用程序和系统架构的元素联系起来。研究旨在研究换能器计算模型及其有效实现，目的是在数据分析系统中提供性能和能效收益，所有这些都依赖于数据转换。特别是，这项工作旨在通过将传感器程序映射到新兴的数据处理加速器来将传感器理论转化为实际应用。为此，这项工作针对以下问题。首先，设计一个软件堆栈，将传感器映射到新的硬件加速器上。特别是，研究人员在他们之前设计和实现非结构化数据处理器的基础上进行了工作，非结构化数据处理器是一种用于数据转换的新型硬件加速器，被证明具有高性能，但目前缺乏高级编程模型。要实现这一目标，需要研究一组独立于平台且特定于平台的优化，旨在最小化代码大小、最小化内存利用率并利用计算中固有的粗粒度和细粒度并行性。第二，根据在软件堆栈设计中获得的见解，改进和扩展底层硬件加速器。第三，扩展传感器模型以表达流行数据分析系统中的全方位数据转换。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。